GateDrain: Availability Attacks and Admission-Side Defense for Confidence-Gated Edge-Cloud Inference

arXiv:2609.33992 · cs.CR · Submitted 2026-09-27 · Read on arXiv

cs.CR

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/hankyul2/EfficientNetV2-pytorch

Terminology

• Accuracy — 56× in this paper · explained in 1 episode
• Calibration — 13× in this paper · explained in 7 episodes
• Trace — 11× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Computer Vision and Pattern Recognition — 3× in this paper
• International Conference on Learning Representations — 3× in this paper
• Abstention — 2× in this paper · explained in 1 episode
• Edge Devices — 2× in this paper · explained in 2 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• ACM Symposium on Edge Computing — 1× in this paper
• Activation Sparsity — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Adversarial Training — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Edge AI — 1× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 1× in this paper
• Mobile Edge Computing — 1× in this paper · explained in 2 episodes
• Resource-constrained Internet of Things — 1× in this paper
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Searching for MobileNetV3
• EfficientNetV2: Smaller Models and Faster Training
• A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
• Overload: Latency Attacks on Object Detection for Edge Devices
• SlowTrack: Increasing the Latency of Camera-based Perception in Autonomous Driving Using Adversarial Examples
• On Calibration of Modern Neural Networks
• Towards Deep Learning Models Resistant to Adversarial Attacks

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